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- W4312309237 abstract "When humans perform object handovers, the non-verbal communication implicit in the movement of the interaction partners mutually communicates information on how the handover will proceed. This intention communication allows both subjects to understand where the transfer of the object will occur, the speed of the gesture, and how careful the receiver of the object must be. In human-robot interaction, it is also desirable that the robot can read and transmit the same information. Bayesian Interaction Primitives (BIP) can be used to learn natural handover interactions from demonstrations performed between humans. In this work, we explore BIPs for handover interactions and compare a state representation obtained directly from a motion capture system with a representation using a statistical body pose model fitted to the motion capture data." @default.
- W4312309237 created "2023-01-04" @default.
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- W4312309237 date "2022-09-12" @default.
- W4312309237 modified "2023-09-29" @default.
- W4312309237 title "Exploiting a Statistical Body Model for Handover Interaction Primitives" @default.
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- W4312309237 doi "https://doi.org/10.1109/icdl53763.2022.9962217" @default.
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